When are the most informative components for inference also the principal components?

@article{Nadakuditi2013WhenAT,
  title={When are the most informative components for inference also the principal components?},
  author={Raj Rao Nadakuditi},
  journal={CoRR},
  year={2013},
  volume={abs/1302.1232}
}
Which components of the singular value decomposition of a signal-plus-noise data matrix are most informative for the inferential task of detecting or estimating an embedded low-rank signal matrix? Principal component analysis ascribes greater importance to the components that capture the greatest variation, i.e., the singular vectors associated with the… CONTINUE READING